A QSAR Model for in Silico Screening of MAO-A Inhibitors. Prediction, Synthesis, and Biological Assay of Novel Coumarins

This work explores the potential of the Mar.-INSIDE methodol. to seek a QSAR for MAO-A inhibitors from a heterogeneous series of compds. A Markov model was used to quickly calc. the mol. electron delocalization, polarizability, refractivity, and n-octanol/water partition coeffs. for a series of 1406 active/nonactive compds. LDA was subsequently used to fit a classification function. The model showed 92.8% and 91.8% global accuracy and predictability in training and validation studies. This QSAR model was validated through a virtual screening of a series of coumarin derivs. The 15 selected compds. were prepd. and evaluated as in vitro MAO-A inhibitors. The theor. prediction was compared with the exptl. results and the model correctly predicted 13 compds. with only two mistakes on compds. with activities very close to the cutoff point established for the model. Consequently, this method represents a useful tool for the "in silico" screening of MAO-A inhibitors.